Short wave frequency band frequency hopping signal blind detection method and system

Abstract

The application provides a short wave frequency band frequency hopping signal blind detection method and system, comprising: collecting spectrum data of short wave full frequency band monitoring, removing pulse interference spectrum and then counting an adaptive threshold; performing signal sorting based on a maximum value spectrum and the adaptive threshold, counting time domain appearance rules and corresponding complete appearance disappearance time of each signal; performing burst signal detection on the sorted basic signals according to a duration, marking burst signal appearance states and the duration, and obtaining a burst signal list; deleting burst signals in the burst signal list that are overdue, and matching standard hopping speeds and duty cycles of the remaining burst signals; performing network station sorting on the matched frequency hopping signals, and obtaining a final frequency hopping detection result. The application is based on wideband monitoring data to count signal changes of adjacent times in real time, to judge whether there is pulse interference, and the spectrum of the pulse interference is not involved in counting and detection, so that the effectiveness of frequency hopping detection of the maximum value spectrum is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic reconnaissance, in particular to a short-wave frequency band frequency hopping signal blind detection method and system. BACKGROUND

[0002] The short-wave frequency band has the characteristic of long transmission distance, and signal transmission of thousands of kilometers can be achieved by ionospheric reflection, so there are many military and civilian communications in the short-wave frequency band in various countries. Frequency hopping communication has the advantages of strong anti-interference and low interception rate, and is widely used in military communication. Various radios using frequency hopping technology are widely used in the military field, greatly improving the anti-interception and anti-interference capability of military equipment. The frequency hopping speed of the short-wave frequency band is usually between 5 hops per second and 200 hops per second.

[0003] Signal detection is the basic function of signal reconnaissance. At present, short-wave signal blind reconnaissance mostly uses direct sampling receivers, which can realize real-time acquisition of full-band 28.5M bandwidth (1.5MHz-30MHz), and output wideband real-time spectrum of full-band 28.5MHz through time-frequency conversion. Short-wave signal detection is based on wideband real-time spectrum to realize signal detection in a specified frequency band, and realizes frequency hopping signal detection and network station sorting based on conventional signal and burst signal detection. The frequency hopping signal can obtain frequency hopping parameters such as frequency hopping speed, frequency hopping frequency set, and signal duty cycle.

[0004] For short-wave frequency band multi-frequency hopping signal network station sorting, most algorithms estimate frequency hopping signal parameters such as frequency hopping speed and frequency hopping frequency set through time-frequency graph processing, but the error of parameter estimation of traditional algorithms is large and the complexity is high. The commonly used frequency hopping signal blind detection method at present is time-frequency analysis technology, including linear time-frequency analysis technology represented by short-time Fourier transform and nonlinear time-frequency analysis technology represented by Wigner-Ville transform. Such algorithms are special detection algorithms for frequency hopping signals. The algorithm performs time-frequency conversion based on original IQ data to obtain a time-frequency graph, and performs processing based on the time-frequency graph to detect frequency hopping signals and estimate parameters.

[0005] Patent document CN116455424A discloses a multi-hop frequency parameter blind estimation method and system based on a second difference algorithm, an IQ data is collected by a reconnaissance platform, a time-frequency diagram of the electromagnetic signal received by the reconnaissance platform is obtained through short-time Fourier transform, signal enhancement of the time-frequency diagram is realized through a frequency domain enhancement process, a clear time-frequency distribution diagram is obtained, and multi-hop frequency signal parameter blind estimation is realized through a second difference algorithm. Patent document CN116979992A discloses a hop period estimation method based on FFT transformation, an IQ data is collected by a reconnaissance platform, and a time-frequency diagram is obtained through processing, a frequency peak value of a frequency hopping pulse is obtained through a time-frequency peak value detection algorithm, a time center sequence of the frequency hopping pulse is created according to the time-frequency peak value, and the time center sequence is processed through FFT transformation, so that the peak value coordinate information related to the hop period can be read in the FFT spectrum after transformation, and the value of the hop period can be calculated according to the coordinate information.

[0006] However, the estimation of the frequency hopping period in the above patent documents is through the average of the frequency hopping period of the multi-hop signal or the FFT transformation of the time center sequence, and these methods are not accurate in the actual detection environment of the short wave frequency band due to the transformation of noise. In view of this, a network frequency hopping signal parameter estimation method based on time-frequency diagram correction is proposed, which can effectively estimate the number of frequency hopping network stations, the hop period, the hop time and the carrier frequency of the received signal. The spectrum in the time-frequency diagram is a short-time spectrum. When the original IQ for time-frequency calculation only contains a part of a hop signal in time, it is equivalent to a pulse signal, and the spectrum will leak, which will appear as a bright line in the time-frequency diagram, thereby affecting the accuracy of subsequent frequency hopping detection.

[0007] The hop speed of the short wave frequency band frequency hopping signal is relatively low, usually between 5 hops per second and 200 hops per second. In actual reconnaissance, due to the large number of short wave frequency band signals and the great influence of ionospheric changes on the transmission of sky waves, the hop speed parameter error estimated by the conventional frequency hopping signal detection algorithm is large. SUMMARY

[0008] In view of the defects in the prior art, the purpose of the present application is to provide a short wave frequency band frequency hopping signal blind detection method and system.

[0009] According to the short wave frequency band frequency hopping signal blind detection method provided by the present application, the following steps are included:

[0010] Step S1: monitoring the antenna perception, collecting the spectrum data of the short wave full frequency band monitoring, and removing the pulse interference spectrum;

[0011] Step S2: statistically determining an adaptive threshold from the spectrum data in the pulse interference spectrum removed;

[0012] Step S3: performing signal sorting based on the maximum spectrum and the adaptive threshold, and statistically determining the time domain appearance rule and the corresponding complete appearance and disappearance time of each signal.

[0013] Step S4: burst signal detection on the sorted base signal according to the duration, marking the burst signal occurrence state and duration, obtaining a burst signal list;

[0014] Step S5: deleting the expired burst signal in the burst signal list, and matching the remaining burst signal with the standard hop speed and duty cycle;

[0015] Step S6: performing network station sorting on the matched frequency hopping signal to obtain the final frequency hopping detection result.

[0016] Preferably, the step S1 comprises:

[0017] Step S1.1: continuously receiving the spectrum data of the shortwave full frequency band monitoring;

[0018] Step S1.2: setting the data statistical period as T cal and the refresh period as T ref ;

[0019] Step S1.3: accumulating multiple frames of full frequency band spectrum data over time, and removing the spectrum with pulse interference when the accumulation period meets and the pulse interference is detected.

[0020] Preferably, the step S1.3 comprises: performing difference operation on the continuous 3 frames of full frequency band spectrum data, and the calculation formula is as follows:

[0021]

[0022] wherein i represents the i-th frame of spectrum, j represents the j-th frequency point, N is the number of points in a frame of spectrum, then it is judged whether the continuous 3 frames of spectrum participating in the operation have pulse interference, and if yes, the frame data is deleted, and if ΔSpec0 and ΔSpec1 are both greater than the judgment threshold, the middle frame data in the 3 frames of data is pulse interference.

[0023] Preferably, the step S2 comprises:

[0024] Step S2.1: statistically calculating the mean spectrum meanSpec, the maximum spectrum maxSpec and the minimum spectrum minSpec, and the formula is as follows:

[0025]

[0026] maxSpec i =MAX(spec j,i )

[0027] minSpec i =MIN(spec j,i )

[0028] wherein meanSpec i denotes the mean spectrum value of the i-th frequency bin, i denotes the i-th frequency bin, spec j,i denotes the value of the i-th frequency bin of the j-th instantaneous spectrum, N is the number of accumulated frames, maxSpec i denotes the maximum spectrum value of the i-th frequency bin, MAX denotes the comparison operation of taking the maximum value, minSpec i denotes the minimum spectrum value of the i-th frequency bin, MIN denotes the comparison operation of taking the minimum value;

[0029] Step S2.2: performing window median filtering on the mean spectrum meanSpec to obtain a basic threshold BasicTh, and the window function width is set to be slightly larger than 2 times the target signal width;

[0030] Step S2.3: counting a floating threshold value addTh, and the formula is as follows:

[0031]

[0032] wherein M denotes the number of frequency bins of a frame of full-band spectrum;

[0033] Step S2.4: calculating an adaptive threshold FinalTh, and the formula is as follows:

[0034] FinalTh = BasicTh + addTh.

[0035] Preferably, the step S3 comprises: comparing the maximum spectrum maxSpec of each frequency bin with the adaptive threshold FinalTh, and marking the frequency bin as a signal frequency bin if the maximum spectrum is higher than the adaptive threshold, otherwise marking the frequency bin as a noise frequency bin; and counting a basic signal BasicSignal if the signal frequency bins are continuous, and recording the start frequency bin BS1 and the end frequency bin BS2 of the basic signal.

[0036] Preferably, the step S4 comprises:

[0037] Step S4.1: taking all the spectra of the basic signal BasicSignal in a statistical period T cal , to obtain a signal spectrum Sig_Spec i , wherein i ∈ [0T cal , and the frequency range of Sig_Spec is BS1 to BS2;

[0038] Step S4.2: performing signal state counting on each frame in Sig_Spec i , and the current time is a signal if the current spectrum is higher than the corresponding adaptive threshold, otherwise the current time is noise;

[0039] Step S4.3: Statistics of all T cal

[0040] Step S4.4: Duration judgment of the marked burst signal, the duration is the last appearance time-first appearance time, when the signal duration is between 0.1 ms to 400 ms, then mark it as burst signal, and add it to the burst signal list S_Puls.

[0041] Preferably, the standard hop rate and duty cycle matching includes the following sub-steps:

[0042] Step S5.1: Match according to hop rate 5 to 200, the current hop rate is StandHopRate, the two-hop time difference StandTime is:

[0043] StandTime = 1 / StandHopRate;

[0044] Step S5.2: Match the frequency hopping signal duty cycle, the duty cycle matching range is 50% to 100%, the step is 5%, the current duty cycle is OccRate, then the theoretical duration of frequency hopping signal per hop StandOccTime is:

[0045] StandOccTime = StandTime * OccRate

[0046] The idle time per hop StandIdleTIme is:

[0047] StandIdleTIme = StandTime - StandOccTime;

[0048] Step S5.3: Match the duration of each signal in the burst signal list in turn, when the deviation ErrTime of signal duration and theoretical duration is less than the duration threshold, then add it to the duration matching queue MeetHop, and calculate the center time HopCenterTime of each hop point, the index HopIndex of the frequency hopping signal in the frequency hopping frequency set, and the error HopErrTime of the center time of the signal, the calculation formulas are as follows:

[0049] ErrTime = |KeepLen - StandOccTime| ​

[0050] HopCenterTime = OffsetTime + 0.5 * KeepLen

[0051] HopIndex = Int(HopCenterTime / StandTime)

[0052] HopErrTime = HopCenterTime - HopIndex * StandTime

[0053] wherein KeepLen represents a duration, Int represents an integer, OffsetTime represents a time interval of each hop signal relative to a latest time signal;

[0054] Step S5.4: If the number of frequency points in the duration matching queue MeetHop is less than 4, return to step S5.2 and increase the duty cycle by steps; if the number of frequency points is greater than or equal to 4, perform regularity screening of the frequency hopping sequence, and execute step S5.5;

[0055] Step S5.5: Cluster the frequency points in the duration matching queue MeetHop according to HopErrTime one by one, and add the frequency points with more than or equal to 4 frequency points after clustering to the same time matching queue MeetHop_Sub i , and calculate the average error MeanErrTimei; wherein i represents the ith group of clustering;

[0056] Step S5.6: Compare the frequency points in the same time matching queue MeetHop_Sub i according to HopIndex one by one, select the frequency hopping frequency point with HopErrTime closer to the average error MeanErrTime i , and add it to the optimal hop point matching queue PreHopList i ;

[0057] Step S5.7: Record the current frequency hopping signal to the frequency hopping signal queue HopRes.

[0058] Preferably, the frequency hopping signal parameters include a hopping speed StandHopRate, a duty cycle OccRate, a duration error mean value MeanKeepLenErr, a root mean square of center time error stdTimeErr, and a frequency hopping frequency parameter.

[0059] The frequency hopping frequency parameter includes a frequency, a bandwidth, a duration, and an occurrence time.

[0060] Preferably, the step S6 comprises sorting the multiple frequency hopping signals in the frequency hopping signal queue HopRes according to the hop rate and the frequency set, judging the same frequency hopping network station signal, refreshing the frequency set and the parameter, if the frequency set has the same frequency, the same occurrence time and the small duration error, merging the frequency set, and taking the measurement result with the small root mean square of the central time error stdTimeErr as the hop rate parameter, otherwise, recording the new frequency hopping signal.

[0061] According to the short wave frequency band frequency hopping signal blind detection system provided by the application, the system comprises:

[0062] Module M1: monitoring antenna sensing, collecting the spectrum data of the short wave full frequency band monitoring, and removing the pulse interference spectrum;

[0063] Module M2: statistically determining the adaptive threshold from the spectrum data in the pulse interference spectrum;

[0064] Module M3: sorting the signals based on the maximum spectrum and the adaptive threshold, and statistically determining the time domain occurrence rule and the corresponding complete occurrence and disappearance time of each signal;

[0065] Module M4: detecting the burst signal according to the duration of the sorted basic signal, marking the burst signal occurrence state and the duration, and obtaining a burst signal list;

[0066] Module M5: deleting the burst signal in the burst signal list that is overdue, and matching the standard hop rate and the duty cycle of the remaining burst signal;

[0067] Module M6: sorting the frequency hopping signal after the matching, and obtaining the final frequency hopping detection result.

[0068] Compared with the prior art, the application has the following beneficial effects:

[0069] 1. The application is based on the short wave frequency hopping signal characteristics, and the signal change in the adjacent time is statistically determined in real time based on the wideband monitoring data to determine whether there is pulse interference, and the spectrum of the pulse interference is not involved in the statistics and detection, so that the effectiveness of the frequency hopping detection of the maximum spectrum is ensured.

[0070] 2. The application uses the hop rate characteristics of the short wave frequency hopping signal to match the hop rate, and the duration error of the frequency set and the central time error of the frequency set are statistically determined to realize the optimal hop rate matching, and then the hop rate parameter accuracy of the short wave frequency band frequency hopping signal is improved.

[0071] 3. The application combines the sorting algorithms of the short wave conventional signal, the burst signal and the frequency hopping signal according to the actual electromagnetic environment, and can realize the blind detection of the short wave frequency band signal. BRIEF DESCRIPTION OF DRAWINGS

[0072] Other features, objects, and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments thereof, when read in conjunction with the accompanying drawings:

[0073] Figure 1 Flow chart for blind detection of short wave frequency band hopping signal of the present application;

[0074] Figure 2 Flow chart for matching of hop rate and duty cycle in the present application;

[0075] Figure 3 Flow chart for burst signal detection in the present application;

[0076] Figure 4 Flow chart for frequency hopping detection-burst signal list in the present application;

[0077] Figure 5 Flow chart for frequency hopping detection-duration matching and error classification in the present application. DETAILED DESCRIPTION

[0078] The present application will be described in detail below with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of changes and improvements can be made. These all belong to the protection scope of the present application.

[0079] The present application realizes frequency hopping signal detection, parameter estimation and network station sorting in short wave frequency band signal wideband detection, uses differential spectrum detection of pulse interference of continuous signal to improve the accuracy of frequency hopping parameter estimation.

[0080] According to the short wave frequency band frequency hopping signal blind detection method provided by the present application, as shown in Figure 1 , comprising:

[0081] Step S1: monitoring antenna sensing, collecting short wave full frequency band monitoring spectrum data, and removing pulse interference spectrum. The step S1 comprises:

[0082] Step S1.1: continuously receiving short wave full frequency band monitoring spectrum data; the spectrum resolution is 1250Hz, and the interval time of each frame of spectrum is 0.8ms.

[0083] Step S1.2: setting the data statistical period as T cal , and the refresh period as T ref , generally taking one third of the statistical period; when the accumulated data satisfies T cal , calculation is performed, after the calculation is completed, the statistical data of T ref is deleted, and accumulation is restarted.

[0084] Step S1.3: Accumulate multi-frame full-band spectrum data over time, when the accumulation period meets and detects pulse interference, the spectrum with pulse interference does not participate in subsequent operation. The step S1.3 comprises: difference operation is performed on continuous 3 frames of full-band spectrum data, and the calculation formula is as follows:

[0085]

[0086] Wherein, i represents the i-th frame spectrum, j represents the j-th frequency point, and N is the number of points in a frame spectrum. Then, it is judged whether the continuous 3 frames of spectrum participating in operation have pulse interference. If yes, the frame data is deleted. If ΔSpec0 and ΔSpec1 are both greater than the judgment threshold, the middle frame data in the 3 frames of data is pulse interference.

[0087] Step S2: Statistically determine the adaptive threshold from the spectrum data in the spectrum with pulse interference removed. The step S2 comprises:

[0088] Step S2.1: Statistically determine the mean spectrum meanSpec, the maximum spectrum maxSpec and the minimum spectrum minSpec, and the formula is as follows:

[0089]

[0090] maxSpec i = MAX (spec j,i )

[0091] minSpec i = MIN (spec j,i )

[0092] Wherein, meanSpec i represents the mean spectrum value of the i-th frequency point, i represents the i-th frequency point, spec j,i represents the value of the i-th frequency point of the j-th instantaneous spectrum, N is the number of accumulated frames, maxSpec i represents the maximum spectrum value of the i-th frequency point, MAX represents comparison operation, and the maximum value is taken, and minSpec i represents the minimum spectrum value of the i-th frequency point, and MIN represents comparison operation, and the minimum value is taken.

[0093] Step S2.2: Window median filtering is performed on the mean spectrum meanSpec to obtain the basic threshold BasicTh, and the window function width is set to be slightly larger than 2 times the target signal width.

[0094] Step S2.3: Statistically determine the floating threshold value addTh, and the formula is as follows:

[0095]

[0096] Wherein, M represents the frequency point number of a frame of full-band spectrum.

[0097] Step S2.4: Calculate the adaptive threshold FinalTh, the formula is as follows:

[0098] FinalTh = BasicTh + addTh.

[0099] Step S3: Based on the maximum spectrum and the adaptive threshold, signal sorting is performed, and the time domain appearance rule and the corresponding complete appearance disappearance time of each signal are counted. The step S3 includes: comparing the maximum spectrum maxSpec of each frequency point and the adaptive threshold FinalTh, if the maximum spectrum is higher than the adaptive threshold, it is marked as a signal frequency point, otherwise it is marked as a noise frequency point; if it is continuously marked as a signal frequency point, it is counted as a basic signal BasicSignal, and the starting frequency point BS1 and the cut-off frequency point BS2 of the basic signal are recorded.

[0100] Step S4: According to the duration, the basic signal sorted out is detected as a burst signal, the burst signal appearance state and the duration are marked, and a burst signal list is obtained. The step S4 includes:

[0101] Step S4.1: Take all the spectra of the basic signal BasicSignal in the statistical period T cal , and obtain the signal spectrum Sig_Spec i , wherein i∈[0T cal , the frequency range of Sig_Spec is BS1 to BS2;

[0102] Step S4.2: Signal state statistics is performed on each frame in Sig_Spec i , if the current spectrum is higher than the corresponding adaptive threshold, the current time is a signal, otherwise it is noise.

[0103] Step S4.3: All T cal signal states are counted, if the current frequency appears continuously, it is a signal, otherwise it is recorded as a new signal, if the signal has complete start-stop time, it is marked as a burst signal, the record state is disappearance, and the signal appearance time, disappearance time and duration KeepLen are recorded, if the head and tail incomplete signal needs to be merged with the last result and the next result, and after merging, the signal is marked according to the signal integrity, as shown in the following formula: Figure 3

[0104] Step S4.4: The duration of the marked burst signal is judged, the duration is the last appearance time-first appearance time, when the signal duration is between 0.1ms and 400ms, it is marked as a burst signal, and is added to the burst signal list S_Puls.

[0105] ​Step S5: deleting the expired burst signals in the burst signal list, and matching the remaining burst signals with the standard hop rate and duty cycle. Specifically, the expired burst signals are filtered out based on the latest data time, and the burst signals exceeding the expiration time are removed from S_Puls; the time interval OffsetTime of each hop signal relative to the latest time signal is calculated; and the frequency hopping of the signals in the burst signal list S_Puls is matched. As shown in Figure 2 the standard hop rate and duty cycle matching includes the following sub-steps:

[0106] Step S5.1: matching according to the hop rate 5 to 200 hops, the current hop rate is StandHopRate, and the time difference between two hops is StandTime = 1 / StandHopRate;

[0107] Step S5.2: matching the duty cycle of the frequency hopping signal, the matching range of the duty cycle is 50% to 100%, the step is 5%, the current duty cycle is OccRate, the theoretical duration of the frequency hopping signal per hop is StandOccTime = StandTime*OccRate, and the idle time per hop is StandIdleTime = StandTime-StandOccTime;

[0108] Step S5.3: sequentially matching the duration of each signal in S_Puls, when the deviation ErrTime of the signal duration and the theoretical duration is less than the duration threshold, the signal is added to the duration matching queue MeetHop, and the center time HopCenterTime of each hop point, the index HopIndex of the frequency hopping signal in the frequency hopping frequency set, and the error HopErrTime of the center time of the signal are calculated. As shown in Figure 2 and Figure 3 the calculation is as follows:

[0109] ErrTime = |KeepLen-StandOccTime|

[0110] HopCenterTime = OffsetTime + 0.5*KeepLen

[0111] HopIndex = Int(HopCenterTime / StandTime)

[0112] HopErrTime = HopCenterTime-HopIndex*StandTime

[0113] wherein Int represents the integer.

[0114] Step S5.4: If the number of frequency points in the duration matching queue MeetHop is less than 4, return to step S5.2 and increase the duty cycle by step; if the number of frequency points is greater than or equal to 4, perform regularity screening of the frequency hopping sequence, and execute step S5.5.

[0115] Step S5.5: Cluster the frequency points in the duration matching queue MeetHop according to HopErrTime in turn, and if the number of frequency points after clustering is greater than or equal to 4, add them to the same time matching queue MeetHop_Sub (i represents the ith group of clustering), and calculate the average error MeanErrTime i .

[0116] Step S5.6: Compare the frequency points in the same time matching queue MeetHop_Sub i according to HopIndex in turn, and select the frequency hopping frequency point with HopErrTime closer to the average error MeanErrTime i of the same HopIndex, and add it to the optimal hop matching queue PreHopList i .

[0117] Step S5.7: Record the current frequency hopping signal in the frequency hopping signal queue HopRes, and the frequency hopping signal parameters include the hop rate StandHopRate, the duty cycle OccRate, the duration error mean MeanKeepLenErr, the root mean square stdTimeErr of the center time error, and the frequency hopping frequency parameters, including frequency, bandwidth, duration, occurrence time, etc., as shown in Figure 4 and Figure 5 , and the specific calculation is as follows:

[0118]

[0119] Wherein, meanKeepLen represents the duration mean, KeepLen represents the duration, MeanKeepLenErr represents the duration error mean, MeanErrTime represents the average error, HopErrTime represents the center time error, stdTimeErr represents the root mean square of the center time error, and N represents the number of frequency points.

[0120] Step S6: The multiple frequency hopping signals in HopRes are sorted according to the hop rate and frequency set, and if the same frequency hopping network signal is determined, the frequency set and parameters are refreshed, and the specific rules are as follows: if the frequency set has the same frequency, the same occurrence time, and the duration error is small, the frequency set is merged, and the hop rate and other parameters are taken as the measurement result with stdTimeErr being small; otherwise, a new frequency hopping signal is recorded.

[0121] The present application aims to solve the problem of identification error caused by pulse interference in the detection of frequency hopping signals in short wave reconnaissance, and improve the accuracy of frequency hopping parameter estimation by using frequency hopping parameter matching.

[0122] Embodiment two

[0123] The present application also provides a short wave frequency band frequency hopping signal blind detection system, which can be realized by executing the flow steps of the short wave frequency band frequency hopping signal blind detection method, that is, the short wave frequency band frequency hopping signal blind detection method can be understood by those skilled in the art as the preferred embodiment of the short wave frequency band frequency hopping signal blind detection system.

[0124] According to the present application, a short wave frequency band frequency hopping signal blind detection system is provided, which comprises:

[0125] Module M1: monitoring antenna sensing, collecting short wave full frequency band monitoring spectrum data, and removing pulse interference spectrum. Module M1 includes: Module M1.1: continuously receiving short wave full frequency band monitoring spectrum data. Module M1.2: setting the data statistical period as T cal , and the refresh period as T ref . Module M1.3: accumulating multiple frames of full frequency band spectrum data over time, and removing the spectrum with pulse interference when the accumulation period meets and detects pulse interference. Module M1.3 includes: performing difference operation on continuous 3 frames of full frequency band spectrum data, and the calculation formula is as follows:

[0126]

[0127] Wherein, i represents the i-th frame spectrum, j represents the j-th frequency point, and N is the number of points in a frame spectrum. Then, it is judged whether the continuous 3 frames of spectrum participating in the operation have pulse interference. If yes, the frame data is deleted. If ΔSpec0 and ΔSpec1 are both greater than the judgment threshold, the middle frame data in the 3 frames of data is pulse interference.

[0128] Module M2: statistically determining an adaptive threshold from the spectrum data in the pulse interference removed spectrum. Module M2 includes: Module M2.1: statistically determining the mean spectrum meanSpec, the maximum spectrum maxSpec and the minimum spectrum minSpec, and the formula is as follows:

[0129]

[0130] maxSpec i =MAX(spec j,i )

[0131] minSpec i =MIN(spec j,i )

[0132] wherein meanSpec i denotes the mean spectrum value of the i-th frequency bin, i denotes the i-th frequency bin, spec j,i denotes the value of the i-th frequency bin of the j-th instantaneous spectrum, N is the number of accumulated frames, maxSpec i denotes the maximum spectrum value of the i-th frequency bin, MAX denotes the comparison operation to take the maximum value, minSpec i denotes the minimum spectrum value of the i-th frequency bin, MIN denotes the comparison operation to take the minimum value. Module M2.2: the mean spectrum meanSpec is median filtered with a window function, and the window function width is set to be slightly larger than twice the target signal width. Module M2.3: the floating threshold value addTh is counted, and the formula is as follows: wherein M denotes the number of frequency bins of a full-band spectrum of a frame. Module M2.4: the adaptive threshold FinalTh is calculated, and the formula is as follows: FinlTh = BasicTh + addTh.

[0133] Module M3: signal sorting is performed based on the maximum spectrum and the adaptive threshold, and the time-domain appearance rule and the corresponding complete appearance and disappearance time of each signal are counted. Module M3 includes: the maximum spectrum maxSpec of each frequency bin is compared with the adaptive threshold FinalTh, and if the maximum spectrum is higher than the adaptive threshold, the frequency bin is marked as a signal frequency bin, otherwise, the frequency bin is marked as a noise frequency bin. If the frequency bins are continuously marked as signal frequency bins, a basic signal BasicSignal is counted, and the starting frequency bin BS1 and the cut-off frequency bin BS2 of the basic signal are recorded.

[0134] Module M4: burst signal detection is performed on the sorted basic signal according to the duration, the burst signal appearance state and the duration are marked, and a burst signal list is obtained. Module M4 includes: Module M4.1: all spectra of the basic signal BasicSignal in the statistical period T cal are taken to obtain signal spectrum Sig_Spec i , wherein i ∈ [0T cal ], and the frequency range of Sig_Spec is BS1 to BS2. Module M4.2: signal state statistics is performed on each frame in Sig_Spec i , and if the current spectrum is higher than the corresponding adaptive threshold, the current time is a signal, otherwise, it is noise. Module M4.3: all T calThe signal state is counted, and the current time is a signal, otherwise it is recorded as a new signal. The signal with complete start-stop time is marked as a burst signal, and the record state is disappeared, and the signal appearance time, disappearance time and duration KeepLen are recorded. The incomplete signal at the head and tail needs to be merged with the last result and the next result, and the signal integrity is marked after merging. Module M4.4: The duration of the marked burst signal is judged, and the duration is the last appearance time-first appearance time. When the signal duration is between 0.1ms and 400ms, it is marked as a burst signal and added to the burst signal list S_Puls.

[0135] Module M5: Delete the burst signal list that exceeds the timeout, and match the remaining burst signal standard hop rate and duty cycle. The standard hop rate and duty cycle matching includes the following sub-modules: Module M5.1: Match according to hop rate 5 to 200 hops, the current hop rate is StandHopRate, and the two-hop time difference StandTime is: StandTime=1 / StandHopRate. Module M5.2: Match the duty cycle of the frequency hopping signal, and the duty cycle matching range is 50% to 100%, with a step of 5%. The current duty cycle is OccRate, then the theoretical duration of the frequency hopping signal per hop StandOccTime is: StandOccTime=StandTime*OccRate, and the idle time per hop StandIdleTIme is: StandIdleTIme=StandTime-StandOccTime. Module M5.3: Match the duration of each signal in the burst signal list in turn, when the deviation ErrTime of signal duration and theoretical duration is less than the duration threshold, add it to the duration matching queue MeetHop, and calculate the center time of each hop point HopCenterTime, the index of the frequency hopping signal in the frequency hopping frequency set HopIndex and the error of the center time of the signal HopErrTime. The calculation formulas are as follows:

[0136] ErrTime=|KeepLen-StandOccTime|

[0137] HopCenterTime=OffsetTime+0.5*KeepLen

[0138] HopIndex=Int(HopCenterTime / StandTime)

[0139] HopErrTime=HopCenterTime-HopIndex*StandTime

[0140] Wherein, KeepLen represents the duration, Int represents the rounding, OffsetTime represents the time interval of each hop signal relative to the latest time signal. Module M5.4: If the number of frequency points in the duration matching queue MeetHop is less than 4, return to module M5.2 and increase the duty cycle according to the step. If the number of frequency points is greater than or equal to 4, the regularity screening of the frequency hopping sequence is performed, and module M5.5 is triggered. Module M5.5: The frequency points in the duration matching queue MeetHop are sequentially clustered according to HopErrTime, and the frequency points after clustering are greater than or equal to 4 frequency points, which are added to the same time matching queue MeetHop_Sub i and the average error MeanErrTimei is calculated. Wherein i represents the i th group of clustering. Module M5.6: The frequency points in the same time matching queue MeetHop_Sub i are sequentially compared according to HopIndex, and the frequency hopping frequency points with the same HopIndex and the average error MeanErrTime i closer are selected, which are added to the optimal hop point matching queue PreHopList i . Module M5.7: The current frequency hopping signal is recorded in the frequency hopping signal queue HopRes. The frequency hopping signal parameters include the hop rate StandHopRate, the duty cycle OccRate, the average error of the duration MeanKeepLenErr, the root mean square of the center time error stdTimeErr and the frequency hopping frequency parameters. The frequency hopping frequency parameters include the frequency, the bandwidth, the duration, the occurrence time.

[0141] Module M6: The matched frequency hopping signal is sorted by network and station, and the final frequency hopping detection result is obtained. Module M6 includes sorting multiple frequency hopping signals in the frequency hopping signal queue HopRes according to the hop rate and the frequency set, judging the same frequency hopping network signal, refreshing the frequency set and the parameters, merging the frequency set if the frequency set has the same frequency, the same occurrence time and the small duration error, and taking the measurement result with the small root mean square of the center time error stdTimeErr as the hop rate parameter. Otherwise, a new frequency hopping signal is recorded.

[0142] Those skilled in the art know that, in addition to implementing the system provided by the present application and each device, module and unit thereof in the form of pure computer readable program code, the system provided by the present application and each device, module and unit thereof can also be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers, etc. by logically programming the method steps to achieve the same functions. Therefore, the system provided by the present application and each device, module and unit thereof can be considered as a hardware component, and the devices, modules and units included therein for achieving various functions can also be considered as structures within the hardware component; the devices, modules and units for achieving various functions can also be considered as both software modules implementing methods and structures within hardware components.

[0143] The specific embodiments of the present application are described above. It needs to be understood that the present application is not limited to the specific embodiments described above, and various changes or modifications can be made by those skilled in the art within the scope of the claims, which does not affect the essential content of the present application. The embodiments of the present application and the features in the embodiments can be combined with each other in any manner without conflict.

Claims

1. A blind detection method for short wave frequency-hopped signals, characterized in that, The method comprises the following steps: Step S1: monitoring antenna sensing, collecting spectrum data of short wave full-band monitoring, and removing pulse interference spectrum; Step S2: counting an adaptive threshold from the spectrum data in the removed pulse interference spectrum; Step S3: performing signal sorting based on the maximum spectrum and the adaptive threshold, counting time domain appearance rules and corresponding complete appearance and disappearance time of each signal; Step S4: performing burst signal detection on the sorted basic signals according to the duration, marking the burst signal appearance state and the duration, and obtaining a burst signal list; Step S5: deleting the burst signals that are overdue in the burst signal list, and matching the remaining burst signals with standard hop rate and duty cycle; Step S6: performing network station sorting on the matched frequency hopping signals, and obtaining a final frequency hopping detection result; The standard hop rate and duty cycle matching comprises the following sub-steps: Step S5.1: matching according to hop rates from 5 to 200 hops, wherein the current hop rate is StandHopRate, and the time difference between two hops is StandTime, which is: StandTime = 1 / StandHopRate; Step S5.2: matching the duty cycle of the frequency hopping signal, wherein the matching range of the duty cycle is 50% to 100%, the step is 5%, the current duty cycle is OccRate, the theoretical duration of the frequency hopping signal per hop is StandOccTime, which is: StandOccTime = StandTime*OccRate The idle time per hop is StandIdleTIme, which is: StandIdleTIme = StandTime-StandOccTime; Step S5.3: sequentially matching the duration of each signal in the burst signal list, when the deviation ErrTime of the signal duration and the theoretical duration is less than the duration threshold, the signal is added to the duration matching queue MeetHop, and the center time HopCenterTime of each hop point, the index HopIndex of the frequency hopping frequency set of the current signal, and the error HopErrTime of the center time of the signal are calculated, and the calculation formulas are as follows: ErrTime = |KeepLen-StandOccTime| HopCenterTime = OffsetTime+0.5*KeepLen HopIndex = Int(HopCenterTime / StandTime) HopErrTime = HopCenterTime-HopIndex*StandTime Wherein, KeepLen represents the duration, Int represents the integer, and OffsetTime represents the time interval of each signal relative to the latest time signal; Step S5.4: if the number of frequency points in the duration matching queue MeetHop is less than 4, return to step S5.2 and increase the duty cycle by steps; if the number of frequency points is greater than or equal to 4, perform regularity screening of the frequency hopping sequence, and execute step S5.

5. Step S5.5: Cluster the frequency points in the duration matching queue MeetHop in turn according to HopErrTime, and if the clustered frequency points are greater than or equal to 4 frequency points, then add them to the same time matching queue MeetHop_Sub, and calculate the average error MeanErrTimei; wherein i represents the i-th group of clustering. i Step S5.6: Cluster the frequency points in the time matching queue MeetHop_Sub in turn according to MeanErrTimei, and if the clustered frequency points are greater than or equal to 4 frequency points, then add them to the same time matching queue MeetHop_Sub, and calculate the average error MeanErrTimei; wherein i represents the i-th group of clustering. Step S5.6: Compare the frequency points of MeetHop_Sub with the frequency points of MeetHop_Sub i in turn according to HopIndex, and select the frequency hopping frequency point of the same HopIndex with HopErrTime closer to MeanErrTime i , and add it to the optimal hop point matching queue PreHopList i . Step S5.7: record the current frequency hopping signal to the frequency hopping signal queue HopRes.

2. The method of blind detection of short wave band frequency hopping signal according to claim 1, characterized in that, The step S1 comprises: Step S1.1: continuously receive the spectrum data of the shortwave full-band monitoring; Step S1.2: set the data statistics period as , and the refresh period as ; Step S1.3: accumulate multiple frames of full-band spectrum data over time, and when the accumulation period meets and pulse interference is detected, remove the spectrum with pulse interference.

3. The method of blind detection of short wave band frequency hopping signal according to claim 2, characterized in that, The step S1.3 comprises: difference operation on 3 continuous frames of full-band spectrum data, and the calculation formula is as follows: Wherein, i indicates the i-th frame spectrum, j indicates the j-th frequency point, N is the number of points of a frame spectrum, then it is judged whether the continuous 3 frames of spectrum participating in operation have pulse interference, if yes, the frame data is deleted, if and are greater than the judgment threshold, the middle frame data in the 3 frame data is pulse interference.

4. The method of blind detection of short wave band frequency hopping signal according to claim 1, characterized in that, The step S2 comprises: Step S2.1: statistics of mean spectrum meanSpec, maximum spectrum maxSpec and minimum spectrum minSpec, and the formula is as follows: wherein, represents the mean spectral value of the i-th frequency point, i represents the i-th frequency point, represents the value of the i-th frequency point of the j-th instantaneous spectrum, N is the number of accumulated frames, represents the maximum spectral value of the i-th frequency point, MAX represents a comparison operation to take the maximum value, represents the minimum spectral value of the i-th frequency point, MIN represents a comparison operation to take the minimum value; Step S2.2: Windowed median filtering the mean spectrum to obtain the base threshold BasicTh, with the window function width set to be slightly larger than 2 times the target signal width. Step S2.3: statistics of floating threshold value addTh, and the formula is as follows: Wherein, M represents the frequency point number of a frame of full-band spectrum; Step S2.4: calculation of adaptive threshold FinalTh, and the formula is as follows: 。 5. The method of blind detection of short wave band frequency hopping signal according to claim 1, characterized in that, The step S3 comprises: comparing the maximum spectrum of each frequency point and an adaptive threshold , if the maximum spectrum is higher than the adaptive threshold, the frequency point is marked as a signal frequency point, otherwise, it is marked as a noise frequency point; if the frequency points are marked as signal frequency points successively, they are counted as a basic signal BasicSignal, and the starting frequency point BS1 and the ending frequency point BS2 of the basic signal are recorded.

6. The method of blind detection of short wave band frequency hopping signal according to claim 1, characterized in that, The step S4 comprises: Step S4.1 : Take all the frequency spectra of the base signal BasicSignal in the statistical period and obtain the signal spectrum where , the frequency range of which is BS1 to BS2; Step S4.2: signal state statistics on each frame in , if the current spectrum is higher than the corresponding adaptive threshold, the current time is signal, otherwise it is noise; Step S4.3: Statistics of all signal states, the current frequency continuous time appears as a signal, otherwise it is recorded as a new signal, and the signal with complete start-stop time is marked as a burst signal. The record state is disappeared, and the signal appearance time, disappearance time and duration KeepLen are recorded. The incomplete head-tail signal needs to be merged with the last result and the next result, and then marked according to the signal integrity. Step S4.4: duration judgment on the marked burst signal, and the duration is the last appearance time-first appearance time, when the signal duration is between 0.1ms to 400ms, it is marked as a burst signal, and added to the burst signal list S_Puls.

7. The method of blind detection of short wave band frequency hopping signal according to claim 1, characterized in that, The frequency hopping signal parameters include a hop rate StandHopRate, a duty cycle OccRate, a mean duration error , a root mean square of center time error stdTimeErr, and a frequency hopping frequency parameter; The frequency hopping frequency parameters include frequency, bandwidth, duration, appearance time.

8. The method of blind detection of short wave band frequency hopping signal according to claim 1, characterized in that, The step S6 includes sorting the multiple frequency hopping signals in the frequency hopping signal queue HopRes according to the hop rate and the frequency set, judging the same frequency hopping network station signal, refreshing the frequency set and the parameter, if the frequency set has the same frequency, the same occurrence time and the small duration error, merging the frequency set, and taking the center time error root mean square as the hop rate parameter Small measurement result; otherwise, record a new frequency hopping signal.

9. A short wave band hopping signal blind detection system, characterized in that, Comprise: Module M1: monitoring antenna sensing, collecting spectrum data of shortwave full-band monitoring, and removing pulse interference spectrum; Module M2: statistics of adaptive threshold from the spectrum data in the pulse interference removed spectrum; Module M3: signal sorting based on maximum spectrum and adaptive threshold, statistics of time domain appearance rule and corresponding complete appearance disappearance time of each signal; Module M4: burst signal detection on the sorted basic signal according to the duration, marking the burst signal appearance state and duration, and obtaining the burst signal list; Module M5: deleting the expired burst signal in the burst signal list, and matching the remaining burst signal with standard hop rate and duty cycle; Module M6: network station sorting on the matched frequency hopping signal, and obtaining the final frequency hopping detection result; The standard hop rate and duty cycle matching comprises the following sub-modules: Module M5.1: matching according to hop rate 5 to 200 hops, the current hop rate is StandHopRate, and the two-hop time difference StandTime is: StandTime=1 / StandHopRate; Module M5.2: matching of frequency hopping signal duty cycle, and the duty cycle matching range is 50% to 100%, the step is 5%, the current duty cycle is OccRate, then the theoretical duration StandOccTime of each hop frequency hopping signal is: StandOccTime=StandTime*OccRate The idle time StandIdleTIme of each hop is: StandIdleTIme=StandTime-StandOccTime; Module M5.3: Match the duration of each signal in the burst signal list in turn, when the deviation ErrTime between the signal duration and the theoretical duration is less than the duration threshold, add it to the duration matching queue MeetHop, and calculate the center time HopCenterTime of each hop point, the index HopIndex of the current signal in the set of frequency hopping frequencies, and the error HopErrTime of the center time of the current signal, the calculation formulas are as follows: ErrTime = |KeepLen-StandOccTime| HopCenterTime = OffsetTime + 0.5 * KeepLen HopIndex = Int(HopCenterTime / StandTime) HopErrTime = HopCenterTime - HopIndex * StandTime Wherein, KeepLen represents the duration, Int represents rounding, OffsetTime represents the time interval of each hop signal relative to the latest time signal; Module M5.4: If the number of frequency points in the duration matching queue MeetHop is less than 4, return to module M5.2 and increase the duty cycle according to the step; if the number of frequency points is greater than or equal to 4, perform regularity screening of the frequency hopping sequence, execute module M5.5; Module M5.5: The frequency points in the duration matching queue MeetHop are clustered according to HopErrTime in sequence, and the clustered frequency points greater than or equal to 4 frequency points are added to the same time matching queue MeetHop_Sub i In the cluster, the average error MeanErrTimei is calculated, where i represents the i-th cluster. Module M5.6: Match the frequency points with the same time duration in sequence to the MeetHop_Sub i According to the HopIndex, the frequency points with the same HopIndex are selected to the frequency hopping frequency points with the HopErrTime closer to the MeanErrTime i of the frequency hopping frequency points, and are added to the PreHopList i ​ Module M5.7: Record the current frequency hopping signal to the frequency hopping signal queue HopRes.

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